There is a method of analyzing a degree (dependence) of how a random variable y depends on another random variable x by using a regression model in an case that the random variable y is assumed to depend on the random variable x. This method is called regression analysis. In regression analysis, the random variable y is called a response variable or a dependent variable. The random variable x is called an explanatory variable or an independent variable.
An example of regression analysis is described in a non-patent literature 1. The regression analysis in the non-patent literature 1 is executed under a condition where a variance of a response variable y is assumed to be constant not depending on a value of an explanatory variable x. In other words, the regression analysis in the non-patent literature 1 is executed under a condition where a variance of a response variable is assumed to be homogeneity in a domain of an explanatory variable.
Further, in a case that both of a mean and a variance of a random variable y are assumed to depend on an observed time t of observing a response variable y, there is a method of analyzing the dependence (degree of how the random variable y depends on another random variable x) by using a general state space model. The general state spatial model is a model which expresses dependence of both the mean and the variance of the response variable y by utilizing time-series data where both of the mean and the variance of the response variable y depend on the observed time t. An example of this analysis is described in a non-patent literature 2.